A Comparative Analysis of Maize and Winter Wheat LAI Retrieval Using Spectral and Texture Features from Sentinel-2A Image.
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| Title: | A Comparative Analysis of Maize and Winter Wheat LAI Retrieval Using Spectral and Texture Features from Sentinel-2A Image. |
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| Authors: | Zhang, Yangyang1 (AUTHOR), Han, Xu1,2 (AUTHOR), Yang, Jian1,2 (AUTHOR) yangjian@cug.edu.cn |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1561. 20p. |
| Subjects: | Leaf area index, Winter wheat, Surface texture, Reflectance, Vegetation monitoring, Corn |
| Abstract: | Highlights: What are the main findings? Red-edge based vegetation indices and mean texture features are robust LAI predictors for both winter wheat and maize, but their relative importance varies by crop. A comparative analysis reveals that optimal texture index combinations are highly crop-specific (e.g., combinations involving variance for wheat vs. contrast for maize), reflecting differences in canopy structure. What are the implications of the main findings? The study demonstrates that while multi-feature fusion improves accuracy, there is no universal model for different crops; strategies must be adapted to specific canopy architectures. This study highlights the necessity of crop-adaptive feature selection—the two-stage optimization significantly boosts accuracy for uniform canopies (wheat) but yields limited gains for heterogeneous canopies (maize), guiding future method development. The leaf area index (LAI) is a key parameter reflecting vegetation canopy structure and growth status. This study systematically compares the performance of spectral and texture features derived from Sentinel-2A imagery for LAI retrieval in winter wheat and maize. Multiple vegetation indices and gray-level co-occurrence matrix (GLCM) texture features were extracted, and three types of texture indices—Normalized Difference Texture Index (NDTI), Ratio Texture Index (RTI), and Difference Texture Index (DTI)—were constructed. Modeling was performed using Partial Least Squares Regression (PLSR) and Gaussian Process Regression (GPR). Results show that red-edge vegetation indices and mean texture features (e.g., NDVI_M) are robust predictors for both crops, with correlation coefficients reaching 0.87 for winter wheat and 0.83 for maize. Texture indices further enhance the representation of canopy structural information; the optimal NDTI achieved |R| > 0.88 for both crops, though the specific feature pairs were crop-specific. Using the proposed two-stage feature optimization strategy combined with GPR, the LAI estimation accuracy for winter wheat reached R2 = 0.87 with RMSE = 0.41 on an independent test set, while for maize the accuracy was R2 = 0.75 with RMSE = 0.38. The strategy significantly improved accuracy for winter wheat (uniform canopy) but yielded limited gains for maize (heterogeneous canopy), largely due to differences in canopy architecture. This study demonstrates that integrating multi-dimensional features with nonlinear modeling enhances LAI estimation accuracy. By providing a side-by-side comparative evaluation across two contrasting crop canopies, this study underscores the necessity of crop-adaptive feature selection and modeling strategies. The findings offer practical guidance rather than a universal model for large-scale crop monitoring in agricultural remote sensing. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Red-edge based vegetation indices and mean texture features are robust LAI predictors for both winter wheat and maize, but their relative importance varies by crop. A comparative analysis reveals that optimal texture index combinations are highly crop-specific (e.g., combinations involving variance for wheat vs. contrast for maize), reflecting differences in canopy structure. What are the implications of the main findings? The study demonstrates that while multi-feature fusion improves accuracy, there is no universal model for different crops; strategies must be adapted to specific canopy architectures. This study highlights the necessity of crop-adaptive feature selection—the two-stage optimization significantly boosts accuracy for uniform canopies (wheat) but yields limited gains for heterogeneous canopies (maize), guiding future method development. The leaf area index (LAI) is a key parameter reflecting vegetation canopy structure and growth status. This study systematically compares the performance of spectral and texture features derived from Sentinel-2A imagery for LAI retrieval in winter wheat and maize. Multiple vegetation indices and gray-level co-occurrence matrix (GLCM) texture features were extracted, and three types of texture indices—Normalized Difference Texture Index (NDTI), Ratio Texture Index (RTI), and Difference Texture Index (DTI)—were constructed. Modeling was performed using Partial Least Squares Regression (PLSR) and Gaussian Process Regression (GPR). Results show that red-edge vegetation indices and mean texture features (e.g., NDVI_M) are robust predictors for both crops, with correlation coefficients reaching 0.87 for winter wheat and 0.83 for maize. Texture indices further enhance the representation of canopy structural information; the optimal NDTI achieved |R| > 0.88 for both crops, though the specific feature pairs were crop-specific. Using the proposed two-stage feature optimization strategy combined with GPR, the LAI estimation accuracy for winter wheat reached R2 = 0.87 with RMSE = 0.41 on an independent test set, while for maize the accuracy was R2 = 0.75 with RMSE = 0.38. The strategy significantly improved accuracy for winter wheat (uniform canopy) but yielded limited gains for maize (heterogeneous canopy), largely due to differences in canopy architecture. This study demonstrates that integrating multi-dimensional features with nonlinear modeling enhances LAI estimation accuracy. By providing a side-by-side comparative evaluation across two contrasting crop canopies, this study underscores the necessity of crop-adaptive feature selection and modeling strategies. The findings offer practical guidance rather than a universal model for large-scale crop monitoring in agricultural remote sensing. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101561 |
